SE-MoLoRA: Shared-Expert LoRA Adapters for Domain-Specific Photographic Assessment

2026-08-18

AI Summary

本文提出SE-MoLoRA框架，通过共享专家和特定领域的适配器分离通用摄影知识与专业判断，以提高视觉-语言模型在特定领域摄影评估中的表现。

Abstract

Vision-language models can describe images fluently, but they often fail to provide actionable photographic critique because semantic content and aesthetic judgment remain entangled. We propose SE-MoLoRA, a modular parameter-efficient adaptation framework for domain-specific photographic assessment. The method separates general photographic knowledge from specialist residual judgments using an always-active shared LoRA expert and routed adapters for composition, lighting, and technical quality. A lightweight query router selects the relevant specialist, enabling targeted critique without training separate full models. A rank-64 shared adapter captures broad photographic vocabulary, while rank-32 specialists learn domain-specific residuals with an orthogonal regularization penalty that encourages disentangled representations. Training data is obtained by distilling the Reddit Photo Critique Dataset into domain-labeled critique samples. On held-out critique generation, SE-MoLoRA improves BERTScore-F1 from 0.2317 to 0.4215 over monolithic LoRA and is preferred in 84.6\% of pairwise comparisons, while using fewer active parameters than separate specialist models. SVD-based ablation study shows that shared-specialist decomposition and orthogonal regularization reduce expert overlap. These results demonstrate that modular adaptation improves controllability and specificity in multimodal photographic critique.

Problem

• photographic assessment
• domain-specific
• visual-language models
• aesthetic judgment
• semantic content

Innovation

• SE-MoLoRA
• modular parameter-efficient adaptation
• shared LoRA expert
• routed adapters
• orthogonal regularization